22 research outputs found

    Recognizing design patterns in C++ programs with the integration of Columbus and Maisa

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    A method for recognizing design patterns from C++ programs is presented. The method consists of two separate phases, analysis and reverse engineering of the C++ code, and architectural pattern matching over the reverse-engineered intermediate code representation. It is shown how the pattern recognition effect can be realized by integrating two specialized software tools, the reverse engineering framework Columbus and the architectural metrics analyzer Maisa. The method and the integrated power of the tool set are illustrated with small experiments

    OF RUPTURES AND RAPTURES: LOCATING IDEOLOGY WITH LIDAR IMAGERY

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    Archaeological praxis necessarily requires at least one object (a piece of technology or something that functions as an object) to articulate and explain ideologies from the past. This is problematic because ideology is abstract and difficult to locate in the archaeological record in reified form. Archaeology’s preoccupation for over 100 years has been the systematic location, identification, and excavation of discrete artifacts; features, and sites; interpreting meanings from comparative studies and data sets; and putting the past in order all while documenting change over time. Historical archaeologist Mark P. Leone identified fences, the Plat of the ideal City of Zion, Mormon temple architecture, plan, and program, and dams on the Little Colorado River in Arizona as the technologies and objects that facilitated Mormon settlement, survival, and adaptation in the Intermontane West of North America in the 19th and early 20th centuries. In 2010, Leone revisited his life-work including a critical re-examination of his original research on Mormon fences, the Plat of the ideal City of Zion, and Mormon temples. In the interim, Leone read Philosopher Slavoj Žižek’s The Sublime Object of Ideology. Žižek defined three types of ideological objects: voids (or absences); large, unattractive objects left over or resultant from the past of which we are all aware; and an index or circulating object, one that is known to exist or have existed and requires an ideological structure to understand it, e.g. Mormonism. Žižek’s definitions and rubric have a potential to answer a research question that emerges out of Leone’s life-work: What was or is the object of Mormon ideological desire in the archaeological record (OMIDAR)? The ultimate ideological desire of 19th and early 20th century Mormonism was the creation of a New Zion. A test revealed that none of the four technologies Leone previously identified completely meets Žižek’s criteria. This dissertation undertook a critical examination of LiDAR imagery of the Mormon Row Historic District (MRHD) (48TE1444) in Grand Teton National Park (GTNP) in which a provisional Mormon irrigation pattern (MIP) was identified. Leone considered irrigation associated with dams as an important factor, but did not consider it as an ideal technological object perhaps because, unlike fences, settlements, temples, and dams, irrigation was not seen as a unary object. The MIP was searched for along the Little Colorado River in Arizona. In each of the settlements in Leone’s original study area at least one relict field containing the MIP was located. As a technology and unary object, the MIP was tested against Žižek’s criteria and it passes. It is averred that the MIP is the metaphysical and material ‘footprint’ of New Zion—an ideological void. The authorizing heritage discourse (AHD) concerning irrigation features is also challenged and a recommended revision concerning their significance and eligibility for listing on the National Register of Historic Places is offered

    Web-based strategies in the manufacturing industry

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    The explosive growth of Internet-based architectures is allowing an efficient access to information resources over geographically dispersed areas. This fact is exerting a major influence on current manufacturing practices. Business activities involving customers, partners, employees and suppliers are being rapidly and efficiently integrated through networked information management environments. Therefore, efforts are required to take advantage of distributed infrastructures that can satisfy information integration and collaborative work strategies in corporate environments. In this research, Internet-based distributed solutions focused on the manufacturing industry are proposed. Three different systems have been developed for the tooling sector, specifically for the company Seco Tools UK Ltd (industrial collaborator). They are summarised as follows. SELTOOL is a Web-based open tool selection system involving the analysis of technical criteria to establish appropriate selection of inserts, toolholders and cutting data for turning, threading and grooving operations. It has been oriented to world-wide Seco customers. SELTOOL provides an interactive and crossed-way of searching for tooling parameters, rather than conventional representation schemes provided by catalogues. Mechanisms were developed to filter, convert and migrate data from different formats to the database (SQL-based) used by SELTOOL.TTS (Tool Trials System) is a Web-based system developed by the author and two other researchers to support Seco sales engineers and technical staff, who would perform tooling trials in geographically dispersed machining centres and benefit from sharing data and results generated by these tests. Through TTS tooling engineers (authorised users) can submit and retrieve highly specific technical tooling data for both milling and turning operations. Moreover, it is possible for tooling engineers to avoid the execution of new tool trials knowing the results of trials carried out in physically distant places, when another engineer had previously executed these trials. The system incorporates encrypted security features suitable for restricted use on the World Wide Web. An urgent need exists for tools to make sense of raw data, extracting useful knowledge from increasingly large collections of data now being constructed and made available from networked information environments. This explosive growth in the availability of information is overwhelming the capabilities of traditional information management systems, to provide efficient ways of detecting anomalies and significant patterns in large sets of data. Inexorably, the tooling industry is generating valuable experimental data. It is a potential and unexplored sector regarding the application of knowledge capturing systems. Hence, to address this issue, a knowledge discovery system called DISKOVER was developed. DISKOVER is an integrated Java-application consisting of five data mining modules, able to be operated through the Internet. Kluster and Q-Fast are two of these modules, entirely developed by the author. Fuzzy-K has been developed by the author in collaboration with another research student in the group at Durham. The final two modules (R-Set and MQG) have been developed by another member of the Durham group. To develop Kluster, a complete clustering methodology was proposed. Kluster is a clustering application able to combine the analysis of quantitative as well as categorical data (conceptual clustering) to establish data classification processes. This module incorporates two original contributions. Specifically, consistent indicators to measure the quality of the final classification and application of optimisation methods to the final groups obtained. Kluster provides the possibility, to users, of introducing case-studies to generate cutting parameters for particular Input requirements. Fuzzy-K is an application having the advantages of hierarchical clustering, while applying fuzzy membership functions to support the generation of similarity measures. The implementation of fuzzy membership functions helped to optimise the grouping of categorical data containing missing or imprecise values. As the tooling database is accessed through the Internet, which is a relatively slow access platform, it was decided to rely on faster Information retrieval mechanisms. Q-fast is an SQL-based exploratory data analysis (EDA) application, Implemented for this purpose

    ConQueSt: a Constraint-based Querying System for Exploratory Pattern Discovery

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    Il contributo di questa tesi è il disegno e lo sviluppo di un sistema di Knoledge Discovery denominato ConQueSt. Basato sul paradigma del Pattern Discovery guidato dai vincoli, ConQueSt segue la visione dell’Inductive Database: • il mining è visto come forma più complessa di querying, • il sistema quindi è equipaggiato con un data mining query language, e strettamente collegato con un DBMS • i pattern estratti con query di mining diventano cittadini di prima classe e, seguendo il principio di chiusura, vengono materializzati accanto ai dati nel DBMS. ConQueSt è già stato presentato con successo al workshop internazionale della comunità IDB, e alla prestigiosa conferenza IEEE International Conference on Data Mining Engineering (ICDE 2006). A giugno sarà presentato alla conferenaz italiana di basi di dati (SEBD 2006). E’ attualmente in corso la sottomissione ad una prestigiosa rivista

    Mining entity and relation structures from text: An effort-light approach

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    In today's computerized and information-based society, text data is rich but often also "messy". We are inundated with vast amounts of text data, written in different genres (from grammatical news articles and scientific papers to noisy social media posts), covering topics in various domains (e.g., medical records, corporate reports, and legal acts). Can computational systems automatically identify various real-world entities mentioned in a new corpus and use them to summarize recent news events reliably? Can computational systems capture and represent different relations between biomedical entities from massive and rapidly emerging life science literature? How might computational systems represent the factual information contained in a collection of medical reports to support answering detailed queries or running data mining tasks? While people can easily access the documents in a gigantic collection with the help of data management systems, they struggle to gain insights from such a large volume of text data: document understanding calls for in-depth content analysis, content analysis itself may require domain-specific knowledge, and over a large corpus, a complete read and analysis by domain experts will invariably be subjective, time-consuming and relatively costly. To turn such massive, unstructured text corpora into machine-readable knowledge, one of the grand challenges is to gain an understanding of the typed entity and relation structures in the corpus. This thesis focuses on developing principled and scalable methods for extracting typed entities and relationship with light human annotation efforts, to overcome the barriers in dealing with text corpora of various domains, genres and languages. In addition to our effort-light methodologies, we also contribute effective, noise-robust models and real-world applications in two main problems: - Identifying Typed Entities: We show how to perform data-driven text segmentation to recognize entities mentioned in text as well as their surrounding relational phrases, and infer types for entity mentions by propagating "distant supervision" (from external knowledge bases) via relational phrases. In order to resolve data sparsity issue during propagation, we complement the type propagation with clustering of functionally similar relational phrases based on their redundant occurrences in large corpus. Apart from entity recognition and coarse-grained typing, we claim that fine-grained entity typing is beneficial for many downstream applications and very challenging due to the context-agnostic label assignment in distant supervision, and we present principled, efficient models and algorithms for inferring fine-grained type path for entity mention based on the sentence context. - Extracting Typed Entity Relationships: We extend the idea of entity recognition and typing to extract relationships between entity mentions and infer their relation types. We show how to effectively model the noisy distant supervision for relationship extraction, and how to avoid the error propagation usually happened in incremental extraction pipeline by integrating typing of entities and relationships in a principled framework. The proposed approach leverages noisy distant supervision for both entities and relationships, and simultaneously learn to uncover the most confident labels as well as modeling the semantic similarity between true labels and text features. In practice, text data is often highly variable: corpora from different domains, genres or languages have typically required for effective processing a wide range of language resources (e.g., grammars, vocabularies, and gazetteers). The “massive” and “messy” nature of text data poses significant challenges to creating tools for automated extraction of entity and relation structures that scale with text volume. State-of-the-art information extraction systems have relied on large amounts of task-specific labeled data (e.g., annotating terrorist attack-related entities in web forum posts written in Arabic), to construct machine-learning models (e.g., deep neural networks). However, even though domain experts can manually create high-quality training data for specific tasks as needed, both the scale and efficiency of such a manual process are limited. This thesis harnesses the power of ``big text data'' and focuses on creating generic solutions for efficient construction of customized machine-learning models for mining typed entities and relationships, relying on only limited amounts of (or even no) task-specific training data. The approaches developed in the thesis are thus general and applicable to all kinds of text corpora in different natural languages, enabling quick deployment of data mining applications. We provide scalable algorithmic approaches that leverage external knowledge bases as sources of supervision and exploit data redundancy in massive text corpora, and we show how to use them in large-scale, real-world applications, including structured exploration and analysis of life sciences literature, extracting document facets from technical documents, document summarization, entity attribute discovery, and open-domain information extraction

    Security-Pattern Recognition and Validation

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    The increasing and diverse number of technologies that are connected to the Internet, such as distributed enterprise systems or small electronic devices like smartphones, brings the topic IT security to the foreground. We interact daily with these technologies and spend much trust on a well-established software development process. However, security vulnerabilities appear in software on all kinds of PC(-like) platforms, and more and more vulnerabilities are published, which compromise systems and their users. Thus, software has also to be modified due to changing requirements, bugs, and security flaws and software engineers must more and more face security issues during the software design; especially maintenance programmers must deal with such use cases after a software has been released. In the domain of software development, design patterns have been proposed as the best-known solutions for recurring problems in software design. Analogously, security patterns are best practices aiming at ensuring security. This thesis develops a deeper understanding of the nature of security patterns. It focuses on their validation and detection regarding the support of reviews and maintenance activities. The landscape of security patterns is diverse. Thus, published security patterns are collected and organized to identify software-related security patterns. The description of the selected software-security patterns is assessed, and they are compared against the common design patterns described by Gamma et al. to identify differences and issues that may influence the detection of security patterns. Based on these insights and a manual detection approach, we illustrate an automatic detection method for security patterns. The approach is implemented in a tool and evaluated in a case study with 25 real-world Android applications from Google Play

    Specifying Reuse Interfaces for Task-Oriented Framework Specialization

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    Reuse of existing carefully designed and tested software improves the quality of new software systems and reduces their development costs. Object-oriented frameworks provide an established means for software reuse on the levels of both architectural design and concrete implementation. Unfortunately, due to frame-works complexity that typically results from their flexibility and overall abstract nature, there are severe problems in using frameworks. Patterns are generally accepted as a convenient way of documenting frameworks and their reuse interfaces. In this thesis it is argued, however, that mere static documentation is not enough to solve the problems related to framework usage. Instead, proper interactive assistance tools are needed in order to enable system-atic framework-based software production. This thesis shows how patterns that document a framework s reuse interface can be represented as dependency graphs, and how dynamic lists of programming tasks can be generated from those graphs to assist the process of using a framework to build an application. This approach to framework specialization combines the ideas of framework cookbooks and task-oriented user interfaces. Tasks provide assistance in (1) cre-ating new code that complies with the framework reuse interface specification, (2) assuring the consistency between existing code and the specification, and (3) adjusting existing code to meet the terms of the specification. Besides illustrating how task-orientation can be applied in the context of using frameworks, this thesis describes a systematic methodology for modeling any framework reuse interface in terms of software patterns based on dependency graphs. The methodology shows how framework-specific reuse interface specifi-cations can be derived from a library of existing reusable pattern hierarchies. Since the methodology focuses on reusing patterns, it also alleviates the recog-nized problem of framework reuse interface specification becoming complicated and unmanageable for frameworks of realistic size. The ideas and methods proposed in this thesis have been tested through imple-menting a framework specialization tool called JavaFrames. JavaFrames uses role-based patterns that specify a reuse interface of a framework to guide frame-work specialization in a task-oriented manner. This thesis reports the results of cases studies in which JavaFrames and the hierarchical framework reuse inter-face modeling methodology were applied to the Struts web application frame-work and the JHotDraw drawing editor framework

    Acta Cybernetica : Volume 15. Number 4.

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    24th International Conference on Information Modelling and Knowledge Bases

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    In the last three decades information modelling and knowledge bases have become essentially important subjects not only in academic communities related to information systems and computer science but also in the business area where information technology is applied. The series of European – Japanese Conference on Information Modelling and Knowledge Bases (EJC) originally started as a co-operation initiative between Japan and Finland in 1982. The practical operations were then organised by professor Ohsuga in Japan and professors Hannu Kangassalo and Hannu Jaakkola in Finland (Nordic countries). Geographical scope has expanded to cover Europe and also other countries. Workshop characteristic - discussion, enough time for presentations and limited number of participants (50) / papers (30) - is typical for the conference. Suggested topics include, but are not limited to: 1. Conceptual modelling: Modelling and specification languages; Domain-specific conceptual modelling; Concepts, concept theories and ontologies; Conceptual modelling of large and heterogeneous systems; Conceptual modelling of spatial, temporal and biological data; Methods for developing, validating and communicating conceptual models. 2. Knowledge and information modelling and discovery: Knowledge discovery, knowledge representation and knowledge management; Advanced data mining and analysis methods; Conceptions of knowledge and information; Modelling information requirements; Intelligent information systems; Information recognition and information modelling. 3. Linguistic modelling: Models of HCI; Information delivery to users; Intelligent informal querying; Linguistic foundation of information and knowledge; Fuzzy linguistic models; Philosophical and linguistic foundations of conceptual models. 4. Cross-cultural communication and social computing: Cross-cultural support systems; Integration, evolution and migration of systems; Collaborative societies; Multicultural web-based software systems; Intercultural collaboration and support systems; Social computing, behavioral modeling and prediction. 5. Environmental modelling and engineering: Environmental information systems (architecture); Spatial, temporal and observational information systems; Large-scale environmental systems; Collaborative knowledge base systems; Agent concepts and conceptualisation; Hazard prediction, prevention and steering systems. 6. Multimedia data modelling and systems: Modelling multimedia information and knowledge; Contentbased multimedia data management; Content-based multimedia retrieval; Privacy and context enhancing technologies; Semantics and pragmatics of multimedia data; Metadata for multimedia information systems. Overall we received 56 submissions. After careful evaluation, 16 papers have been selected as long paper, 17 papers as short papers, 5 papers as position papers, and 3 papers for presentation of perspective challenges. We thank all colleagues for their support of this issue of the EJC conference, especially the program committee, the organising committee, and the programme coordination team. The long and the short papers presented in the conference are revised after the conference and published in the Series of “Frontiers in Artificial Intelligence” by IOS Press (Amsterdam). The books “Information Modelling and Knowledge Bases” are edited by the Editing Committee of the conference. We believe that the conference will be productive and fruitful in the advance of research and application of information modelling and knowledge bases. Bernhard Thalheim Hannu Jaakkola Yasushi Kiyok
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